A distribution automation communication signal optimization method and system for underground environments

Abstract

The present invention relates to the field of communication signal optimization technology, and in particular to a distribution automation communication signal optimization method and system for underground environments. The method comprises the following steps: performing a multi-band data transmission test based on a perturbation frequency test package to collect a multi-band distribution communication signal; and performing a dynamic spatiotemporal attenuation analysis to construct a multi-band electromagnetic spatiotemporal attenuation matrix; performing node-by-node response delay calculation based on the multi-band distribution communication signal to generate a full-band delay sensitivity curve; performing a safe and effective frequency band assessment based on the multi-band distribution communication signal, and performing a high-delay frequency band elimination process based on the full-band delay sensitivity curve to construct a frequency band hopping optimization pool; performing a real-time environmental interference attenuation analysis on the frequency band hopping optimization pool according to the multi-band electromagnetic spatiotemporal attenuation matrix to construct a communication frequency band hopping drive logic. The present invention improves the communication stability and quality of distribution automation through dynamic and intelligent communication frequency band hopping.

Description

Technical Field

[0001] The present invention relates to the technical field of communication signal optimization, and in particular to a distribution automation communication signal optimization method and system for underground environments. Background Art

[0002] With the accelerating pace of urbanization, underground space is being developed and utilized more extensively, encompassing a variety of infrastructure forms such as subways, tunnels, and underground utility corridors. In this context, distribution automation systems, as a crucial technical means to ensure stable power supply and intelligent management, are gradually being deployed underground. Distribution automation systems rely on efficient and reliable communication technologies to enable remote monitoring, data collection, and fault response of distribution equipment. Therefore, the quality and stability of communication signals are particularly critical in underground environments. However, compared to open surface environments, underground spaces are highly enclosed, complex, and contain a high concentration of electromagnetic interference sources. These factors significantly impact the transmission quality of communication signals within distribution automation systems. For example, repeated signal reflections and attenuation between tunnel walls can easily lead to packet loss, increased latency, and even communication interruptions. Furthermore, the parallel operation of multiple electrical equipment and communication systems underground can easily cause spectrum overlap and interference, further weakening the stability and reliability of communication links.

[0003] Currently, traditional methods for ensuring communication in underground distribution automation rely on increasing signal transmission power, installing signal relay equipment, or adopting communication protocols with strong interference resistance. While these methods have improved communication performance to a certain extent, they still have many limitations. For example, increased power often comes with higher energy consumption and equipment heat generation; relay equipment increases system complexity and maintenance costs; and fixed protocols are difficult to dynamically adjust to the environment, making them difficult to cope with the complex and changing underground communication environment. Therefore, traditional methods often struggle to balance energy efficiency, flexibility, and real-time performance while ensuring communication quality. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention proposes a distribution automation communication signal optimization method and system for underground environments to solve at least one of the above technical problems.

[0005] To achieve the above object, the present invention provides a method for optimizing communication signals for distribution automation in an underground environment, comprising the following steps:

[0006] Step S1: Perform a multi-band data transmission test based on a perturbation frequency test package to collect multi-band power distribution communication signals; perform dynamic spatiotemporal attenuation analysis to construct a multi-band electromagnetic spatiotemporal attenuation matrix;

[0007] Step S2: performing node-by-node response delay calculation based on the multi-band power distribution communication signal, and performing frequency delay sensitivity evaluation to generate a full-band delay sensitivity curve;

[0008] Step S3: performing a safe and effective frequency band assessment based on the multi-band power distribution communication signal, and eliminating high-delay frequency bands based on the full-band delay sensitivity curve to construct a frequency band hopping optimization pool;

[0009] Step S4: performing real-time environmental interference attenuation analysis on the frequency band hopping optimization pool according to the multi-band electromagnetic spatiotemporal attenuation matrix, then making a nonlinear frequency band hopping driving decision, and constructing the communication frequency band hopping driving logic;

[0010] Step S5: Performing instant frequency hopping control according to the communication frequency band hopping driving logic, assigning an independent frequency hopping behavior vector to the power distribution communication node, and generating an adaptive hopping instruction sequence;

[0011] Step S6: Perform state synchronization reconstruction based on the adaptive jump instruction sequence, and perform dynamic deviation correction and delay repair on the communication frequency band jump driving logic to perform distribution automation communication signal optimization operations.

[0012] In this specification, a distribution automation communication signal optimization system for an underground environment is provided, which is used to execute the distribution automation communication signal optimization method for an underground environment as described above, including:

[0013] The space-time attenuation module is used to perform multi-band data transmission tests based on perturbation frequency test packages, collect multi-band power distribution communication signals, perform dynamic space-time attenuation analysis, and construct a multi-band electromagnetic space-time attenuation matrix.

[0014] A delay sensitivity module, configured to calculate the response delay of each node based on the multi-band power distribution communication signal, perform frequency delay sensitivity evaluation, and generate a full-band delay sensitivity curve;

[0015] A frequency band elimination module is used to perform safe and effective frequency band evaluation based on the multi-band power distribution communication signal, and eliminate high-delay frequency bands based on the full-band delay sensitivity curve to build a frequency band hopping optimization pool;

[0016] The frequency band hopping drive module is used to perform real-time environmental interference attenuation analysis on the frequency band hopping optimization pool based on the multi-band electromagnetic spatiotemporal attenuation matrix, and then make nonlinear frequency band hopping drive decisions to build the communication frequency band hopping drive logic;

[0017] The frequency hopping control module is used to perform real-time frequency hopping control according to the communication frequency band hopping drive logic, give the power distribution communication node an independent frequency hopping behavior vector, and generate an adaptive hopping instruction sequence;

[0018] The signal control optimization module is used to perform state synchronization reconstruction based on the adaptive jump instruction sequence, and dynamically correct the communication frequency band jump drive logic and repair the delay to perform distribution automation communication signal optimization operations.

[0019] The beneficial effects of the present invention include: Through multi-band signal acquisition, a comprehensive understanding of the attenuation characteristics, noise interference, and channel stability of different frequency bands during transmission in underground environments is achieved. Constructing an electromagnetic spatiotemporal attenuation matrix reflects the temporal and spatial variations of signals in underground power distribution communication environments, providing data support for subsequent frequency band optimization and hopping strategies. Given the complex and ever-changing underground environment, this step helps enhance the system's communication adaptability to diverse electromagnetic environments and reduce bit error rates. Measuring the response delay of each communication node individually enables precise identification of latency bottleneck nodes and channels. The full-band delay curve generated by delay sensitivity assessment helps identify frequency bands that significantly impact communication stability, improving the system's ability to predict high latency risks. This supports the optimization of delay tolerance in subsequent hopping strategies, ensuring the real-time communication requirements of underground power distribution automation. Frequency bands with high latency and susceptibility to interference are removed to avoid critical communications on these bands, effectively improving communication quality. By evaluating the remaining frequency bands, a set of frequency bands with high availability and low latency is identified, providing efficient spectrum resources for the frequency hopping mechanism. Filtering out frequency bands with minimal electromagnetic interference reduces the probability of communication interruption or data loss, thereby improving system robustness. Taking into account dynamic environmental interference, a nonlinear hopping drive logic is constructed to enable intelligent switching of communication frequency bands. This allows for dynamic avoidance of frequency bands containing interference sources, ensuring communication link stability. Instead of relying on a static frequency hopping list, the system utilizes real-time perception and policy-driven approaches, enhancing the adaptability of the communication system in complex underground environments. An independent frequency hopping behavior vector is generated for each communication node, enabling differentiated and coordinated optimization of frequency hopping strategies between nodes. A rational hopping sequence avoids spectrum resource conflicts and improves the overall spectrum utilization of the multi-node communication system. The frequency hopping behavior offers stealth features against eavesdropping and interference, enhancing the confidentiality and stability of the communication link. During frequency hopping, a state synchronization and reconstruction mechanism ensures consistency among all communication nodes, preventing data desynchronization or link interruption. Real-time deviation correction and delay repair mechanisms adjust the frequency hopping logic based on environmental changes, enhancing the system's self-healing capabilities. Over long-term operation, the system can continuously iteratively optimize the hopping logic and frequency band selection, achieving continuous improvement in communication performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a schematic flow chart of the steps of a method for optimizing communication signals for power distribution automation in an underground environment according to the present invention;

[0021] Figure 2 Detailed implementation flow chart of step S1;

[0022] Figure 3 Detailed implementation flow chart of step S2;

[0023] Figure 4 Schematic diagram of the detailed implementation steps of step S3. DETAILED DESCRIPTION

[0024] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0025] This application example provides a method and system for optimizing communication signals for distribution automation in underground environments. The execution entities of the method and system for optimizing communication signals for distribution automation in underground environments include, but are not limited to, the following: mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc., which can be considered as general computing nodes of this application. The data processing platform includes, but is not limited to, at least one of an audio and image management system, an information management system, and a cloud data management system.

[0026] See also Figures 1 to 4 The present invention provides a method for optimizing distribution automation communication signals for underground environments, comprising the following steps:

[0027] Step S1: Perform a multi-band data transmission test based on a perturbation frequency test package to collect multi-band power distribution communication signals; perform dynamic spatiotemporal attenuation analysis to construct a multi-band electromagnetic spatiotemporal attenuation matrix;

[0028] Step S2: performing node-by-node response delay calculation based on the multi-band power distribution communication signal, and performing frequency delay sensitivity evaluation to generate a full-band delay sensitivity curve;

[0029] Step S3: performing a safe and effective frequency band assessment based on the multi-band power distribution communication signal, and eliminating high-delay frequency bands based on the full-band delay sensitivity curve to construct a frequency band hopping optimization pool;

[0030] Step S4: performing real-time environmental interference attenuation analysis on the frequency band hopping optimization pool according to the multi-band electromagnetic spatiotemporal attenuation matrix, then making a nonlinear frequency band hopping driving decision, and constructing the communication frequency band hopping driving logic;

[0031] Step S5: Performing instant frequency hopping control according to the communication frequency band hopping driving logic, assigning an independent frequency hopping behavior vector to the power distribution communication node, and generating an adaptive hopping instruction sequence;

[0032] Step S6: Perform state synchronization reconstruction based on the adaptive jump instruction sequence, and perform dynamic deviation correction and delay repair on the communication frequency band jump driving logic to perform distribution automation communication signal optimization operations.

[0033] In the embodiment of the present invention, see Figure 1 , is a schematic flow chart of the steps of a method for optimizing communication signals for distribution automation in an underground environment according to the present invention. In this example, the steps of the method include:

[0034] Step S1: Perform a multi-band data transmission test based on a perturbation frequency test package to collect multi-band power distribution communication signals; perform dynamic spatiotemporal attenuation analysis to construct a multi-band electromagnetic spatiotemporal attenuation matrix;

[0035] In this embodiment, a test data packet with frequency perturbation characteristics, namely a "perturbation frequency test packet," is constructed. This test packet is designed with a multi-band synchronous modulation structure, covering the three main communication frequency bands: low frequency (30kHz-90kHz), medium frequency (90kHz-300kHz), and high frequency (300kHz-1MHz). Each frequency band uses constant carrier modulation and inserts preset perturbation factors (such as amplitude fluctuations, frequency hopping perturbation bits, and power perturbation sequences) to simulate transient interference and fluctuation scenarios that may be encountered in underground power distribution systems. Each test cycle lasts approximately 5 minutes. The test packet is broadcast periodically through the communication module of the distribution node, and the received signal is recorded by multiple surrounding receiving nodes. The average spacing between nodes is 25 meters. The test path is selected in typical enclosed or semi-enclosed areas such as underground ventilation shafts, tunnel mezzanines, and under cable trays to simulate a real-world distribution automation communication environment. During the test packet transmission process, key communication parameters such as signal strength (RSSI), instantaneous received power, carrier-to-noise ratio (SNR), and signal duration are recorded within each frequency band. Data is continuously collected with a sampling period of 20ms. In high-noise areas, such as those around high-voltage equipment and densely intersecting cables, test packet reception exhibits frequent fluctuations. By performing testing in frequency bands, the system fully covers the primary and backup frequency ranges likely used for power distribution communications, providing raw data support for subsequent attenuation characteristic modeling. After a test packet is successfully transmitted and received by multiple nodes, the system collects the communication signals received by each receiving node across all frequency bands. Each node's received data is tagged with a timestamp, receiving frequency band, signal strength, relative phase, and reception duration, and synchronized to the network's central clock time through a synchronization mechanism. Crucially, each node's received signal is annotated with a spatial location, employing a combined identification method based on underground inertial navigation and topological location mapping. Because GPS signals are difficult to effectively use underground, positioning relies primarily on pre-deployment building blueprints, cable routing diagrams, manual inspection coordinates, and inertial measurement unit (IMU) recognition. Each node's position accuracy is maintained within ±1.2 meters. The spatial topology between nodes is constructed as a 3D mesh model. All spatial locations of received signals are connected via a communication link diagram, forming a multi-band signal propagation data structure. Taking test point A as an example, during mid-frequency (120kHz) communication, the signal exhibited significant attenuation along the transmission path from node A to node E. This path traverses a low-voltage distribution shaft, indicating an interference focus in this area. The system aggregated the multi-band signal strengths received at different nodes and at different times into time segments (30-second windows), forming a three-dimensional frequency-band-time-location dataset. Next, a sliding window statistical analysis method was used to model the signal strength trends and identify their stability and variation within specific frequency bands. Within a tunnel section, the mid-frequency (150kHz) signal exhibited periodic attenuation, with signal strength dropping by 3-5dB every 60 seconds. This was initially associated with intermittent activation of underground equipment.

[0036] Furthermore, to eliminate human interference or equipment errors, adaptive filtering methods such as Kalman filtering or variable structure filtering are employed to smooth outliers and improve data modeling accuracy. Ultimately, attenuation trend data across all frequency bands, locations, and time axes are uniformly categorized, extracting the attenuation slope, fluctuation range, and time delay characteristics for each frequency band at each spatial location. This analysis constructs a three-dimensional "frequency band-location-time" feature matrix, which serves as a core input for the subsequent frequency hopping control strategy. Experiments using this matrix, constructed at over 120 test nodes, demonstrated the non-uniform propagation characteristics of electromagnetic signals in typical urban underground cable tunnels, validating the importance of frequency selection and environmental awareness. The system further normalizes the three-dimensional feature data into a "multi-band electromagnetic spatiotemporal attenuation matrix" for decision-making. Each element of this matrix represents the average attenuation rate and fluctuation range within a specific frequency band, node location, and time period, and is labeled with a stability level. The matrix utilizes a modular architecture and supports real-time dynamic updates, refreshing the current communication status and attenuation level of each node on a minute-by-minute basis. Taking node N23 as an example, its average attenuation in the high frequency band (850kHz) from 0:00 to 2:00 at night reaches 11dB, an improvement of 45% compared to daytime. The matrix immediately updates its nighttime frequency band usage weight.

[0037] Step S2: performing node-by-node response delay calculation based on the multi-band power distribution communication signal, and performing frequency delay sensitivity evaluation to generate a full-band delay sensitivity curve;

[0038] In this embodiment, the communication process of perturbation frequency test packets over multi-band channels is time-series recorded. The system first records the precise timestamp of each perturbation frequency test packet transmission at the master transmitting node. This timestamp is generated by the master communication module, equipped with a high-precision clock source (such as a temperature-compensated crystal oscillator module with a frequency stability within ±0.5ppm), ensuring synchronization consistency across the entire network. Each receiving node also records the corresponding reception timestamp after receiving the communication signal. To ensure data timing consistency, both the receiving node and the master node utilize the Network Time Protocol (NTP) synchronization mechanism, combined with local clock offset correction logic, to keep inter-node time errors within 1 millisecond. During the experiment, signal arrival times were recorded on 10 frequency bands at 50 nodes, resulting in over 600,000 timestamp data points. Each data point contains parameters such as the node number, frequency identifier, transmission time, reception time, and reception status, providing a complete data source for subsequent delay calculations. Given the send and receive timestamps, the system calculates the communication response delay for each node in each frequency band. This is the total time it takes for a data packet to be sent from the master node to be received by the destination node. This calculation takes into account factors such as signal propagation delay, network protocol response delay, and node decoding processing delay.

[0039] After preliminary data screening, invalid records due to communication interruptions, severe bit errors, or signal loss were removed. The remaining valid data was divided by node and frequency band to construct a two-dimensional "node-frequency band" delay model. Taking node N15 as an example, its average delay in the mid-frequency band (180kHz) was 7.6ms, while in the high-frequency band (900kHz), it was 12.3ms, demonstrating significant frequency sensitivity. The delay data for each frequency band was segmented using a time sliding window approach (5 minutes per window, 1 minute sliding step). The average, maximum, and fluctuation range of the delay within different time periods were extracted for further analysis of communication stability and frequency band reliability. The delay variations of the same node over different time periods were analyzed to identify its temporal dynamic characteristics. The delay change rate comparison method was used across multiple time windows to calculate the delay variation difference between adjacent windows, focusing on identifying frequency bands with abnormal fluctuations or sudden increases. In test scenarios, the system observed that in humid environments or when strong electromagnetic equipment was operating (such as in the power distribution area of ​​a subway station), the delay fluctuation in the high-frequency band could reach ±5ms, far exceeding the ±1ms range for the same node in the low-frequency band. These differences are aggregated to form a three-dimensional node-frequency-band-time delay variation feature set. This set is further classified using cluster analysis algorithms (such as K-means or DBSCAN) to delineate frequency-band-sensitive and stable regions, providing a basis for subsequent frequency sensitivity analysis. After establishing a model of multi-band response delays and their temporal variation characteristics, the system conducts delay sensitivity analysis, evaluating the responsiveness of communications in each frequency band to delay fluctuations. This process, focusing on frequency bands, aggregates and analyzes delay data from different nodes within the same frequency band. The sensitivity of the frequency band to environmental variations is then assessed by combining delay differences between time windows. The delay variation range for the 260kHz frequency band across multiple nodes is consistently within ±1ms, indicating insensitivity to environmental perturbations. However, the 750kHz frequency band exhibits fluctuations exceeding ±6ms in some densely wired underground areas, demonstrating high sensitivity. The sensitivity assessment results for all frequency bands are categorized into five levels of response stability (very stable, relatively stable, neutral, relatively sensitive, and very sensitive) and plotted as frequency-delay sensitivity curves. This curve clearly reflects the delay performance trend in the frequency dimension and serves as an important decision-making reference for subsequent frequency band hopping optimization and path reconstruction.

[0040] Step S3: performing a safe and effective frequency band assessment based on the multi-band power distribution communication signal, and eliminating high-delay frequency bands based on the full-band delay sensitivity curve to construct a frequency band hopping optimization pool;

[0041] In this embodiment, the system extracts key communication quality indicators, primarily bit error rate (BER) and packet loss rate (PLR), from previously collected multi-band power distribution communication signals, based on the data packet transmission status of each frequency band. The BER is measured by comparing the difference between the checksum in the test packet and the actual received data, while the PLR ​​measures the percentage of data packets lost within a set time period within the same frequency band. The experiment evaluated 10 frequency bands (100kHz to 1000kHz), sampling at 100 nodes, with each node receiving at least 3000 test packets per frequency band. In certain high-frequency bands (such as 860kHz), due to environmental coupling interference and cable impedance, the BER reached as high as 4.2% at some nodes, while the PLR ​​exceeded 5%. In contrast, frequency bands such as 320kHz and 440kHz exhibited stable BERs below 0.5% and virtually zero packet loss. By setting safety thresholds of 2% for bit error rate and 3% for packet loss rate, the system filters and labels the performance of all frequency bands at different nodes, initially eliminating those with severe signal quality issues. This step ensures the selected frequency bands have basic communication availability and provides a safety baseline for subsequent frequency hopping pool construction. After initially screening the safe frequency bands, the system further uses the "all-band delay sensitivity curve" constructed in step S2 as an evaluation basis to conduct a delay stability analysis on the remaining frequency bands. Each frequency band's delay sensitivity level is assigned a weighting factor to assess its availability and priority in dynamic frequency hopping. Frequency bands exhibiting significant delay fluctuations (e.g., fluctuations exceeding ±3ms) or maximum delays exceeding 8ms across multiple time windows are labeled "high-latency bands" by the system. In areas with dense node distribution and complex underground environments, frequency bands such as 680kHz and 780kHz were identified as delay-sensitive or excessively high-latency bands in more than 20% of the test nodes. After summarizing these delay evaluation data, the system combines the aforementioned bit error rate and packet loss rate information to score each frequency band and form a "frequency band stability score sheet". After identifying high-latency frequency bands, the system removes these frequency bands from the preliminary set of safe communication frequency bands to prevent them from being used in the hopping process. This removal operation is not only based on the static analysis of the delay data, but also combines its time dynamic evolution characteristics to prevent the frequency band from showing unpredictable communication bottlenecks within a specific period. The remaining frequency band set is the "frequency band hopping preferred pool" after removing high bit error rate, high packet loss rate and high delay frequency bands. This preferred pool represents a set of frequency bands that still have communication security, delay stability and anti-interference capabilities after multi-dimensional evaluation in the current underground power distribution communication environment.

[0042] Step S4: performing real-time environmental interference attenuation analysis on the frequency band hopping optimization pool according to the multi-band electromagnetic spatiotemporal attenuation matrix, then making a nonlinear frequency band hopping driving decision, and constructing the communication frequency band hopping driving logic;

[0043] In this embodiment, for each preferred frequency band, the signal strength attenuation trajectory over different time periods was extracted according to the spatial node dimension, constructing a three-dimensional attenuation feature set based on the frequency band, node, and time. In a densely populated underground cable section of the test area, frequency bands 320kHz, 420kHz, and 560kHz were sampled at 30 nodes over a 30-minute period, with a two-minute sampling interval. The mean, maximum, and rate of change of the signal attenuation values ​​in the time series were compared. The attenuation matrix revealed significant high-frequency microwave interference in some regional nodes of the 560kHz band, with signal attenuation as high as 11dB. In contrast, the fluctuation of the 320kHz band at the same location was only 4dB. This method can quickly identify the degree to which a frequency band is affected by the surrounding environment (such as transformers, cable circulation, and geomagnetic disturbances), and extract an "environmental adaptability" indicator for each frequency band in its current state, providing a dynamic basis for subsequent hopping decisions. After extracting the spatiotemporal attenuation features, the system enters the dynamic stability assessment phase of the frequency band. This assessment dynamically grades the status of each frequency band based on the changes in the attenuation trend slope and fluctuation range within the time series data, forming a "attenuation evolution signature." This stage uses a sliding time window (e.g., 10 minutes) and multi-node regional averaging to quantify persistent degradation (signal attenuation) or short-term, sharp fluctuations in frequency band performance. During the test, the 420kHz band experienced a sudden, transient attenuation peak (greater than 9dB) near a cable intersection, but quickly recovered after 10 minutes, demonstrating good attenuation resilience. In contrast, the attenuation of the 560kHz band continued to increase, indicating poor stability. After determining the environmental adaptability and dynamic stability rating of each preferred frequency band, the system prioritizes the pool of preferred frequency bands. This ranking is based on multiple criteria: delay sensitivity (derived from S2), error and packet loss metrics (derived from S3), and environmental interference response capabilities extracted in the current step. Band priority is calculated using a weighted comprehensive ranking mechanism, with dynamic weight adjustments ensuring flexible and adaptable selection of the optimal frequency band under varying environmental conditions. When environmental interference is the primary factor, the system prioritizes frequency bands with higher environmental adaptability (such as 320kHz). When response delays affect communication control, frequency bands with stable latency are prioritized. This sequence is not fixed but updated in real time, reflecting the nonlinear and time-varying nature of frequency hopping decisions. After sorting, high-priority frequency bands are included in the next frequency hopping logic driver module. The system generates frequency hopping driver logic based on the hopping priority list and the current node's communication status (such as receive packet loss rate, link latency peak, and local interference signals). This logic nonlinearly regulates the frequency hopping process, rather than simply rotating it periodically. The driver logic is implemented by monitoring communication status anomaly indicators within the current time window. When these indicators exceed dynamic thresholds (such as latency exceeding 10ms or packet loss rate exceeding 3% for consecutive periods), the system triggers a frequency hopping action and selects the next frequency band based on the priority list.The system achieves a frequency-hopping drive mechanism that is "locally adaptive and globally dynamic and coordinated" by synchronously mapping the hopping logic with node location, interference event history, and current communication quality. Experiments have shown that adopting this hopping logic in complex underground communication environments reduces average inter-node latency by 21%, bit error rates by 16%, and significantly improves communication stability.

[0044] Step S5: Performing instant frequency hopping control according to the communication frequency band hopping driving logic, assigning an independent frequency hopping behavior vector to the power distribution communication node, and generating an adaptive hopping instruction sequence;

[0045] In this embodiment, during the continuous operation of the communication system, a real-time monitoring mechanism continuously evaluates the current communication status, including signal delay, bit error rate, and frequency interference intensity. When these indicators trigger the frequency hopping threshold set by the hopping logic (this threshold is dynamically generated in step S4), the system immediately initiates the immediate frequency hopping response process. This response mechanism relies on the communication master control module to issue instructions to all nodes and inform them of the latest list of available hopping frequency bands in the corresponding frequency band priority pool. In the experiment, 15 power distribution communication nodes were deployed in a simulated tunnel environment, each equipped with a frequency band modulation module. The monitoring system detected that the bit error rate of the 420kHz band at nodes A and C continued to increase. After exceeding the hopping threshold, the system, based on the current hopping priority logic, determined that they should immediately switch to the 320kHz band. In this case, nodes A and C synchronously entered the frequency hopping preparation state through the built-in frequency hopping control module. To enhance the system's resilience in complex underground environments, the communication system does not adopt a centralized "unified frequency hopping" control mode, but instead builds a distributed frequency hopping behavior mechanism centered on "node-adaptive frequency band adjustment." Even if multiple nodes are in the same physical area, their frequency hopping behavior can be individually optimized based on their communication quality data and environmental conditions. This frequency hopping behavior is defined as a "frequency hopping behavior vector," which includes the target frequency band to hop to, the hopping trigger delay, the frequency band's available period, the interference coefficient, and the delay risk value. In the experiment, although Node B was physically close to Node A, the attenuation of the 420kHz signal it detected was not significant. Therefore, its frequency hopping behavior vector did not include an immediate switching action, and it remained in a wait-and-see state.

[0046] By embedding a micro-algorithm module on the node side, it can independently analyze hopping instructions and real-time parameters, autonomously deciding whether to execute the hopping action. This achieves intelligent decoupling and regional autonomy of frequency hopping control. This mechanism effectively avoids centralized frequency resource congestion, improving the system's overall anti-interference capability and communication efficiency. After the frequency hopping drive logic determines the hopping action and the node behavior vector is established, the system begins generating the specific hopping control instruction sequence. This instruction sequence includes the hopping execution timestamp, target frequency band code, backup frequency band link identifier, and synchronization verification information. All hopping instructions are broadcast from the master node to the slave nodes. In some scenarios, segmented distribution is also used to improve the robustness of instruction transmission. In the experimental setup, the system adopts a master-multiple slave architecture, with the master node controlling hopping synchronization. Taking a 320kHz switch as an example, the instruction sequence is issued at T0, and the node must enter the frequency band switching state within T0 + 5ms. Upon receiving the hopping instruction, the slave node immediately responds according to its frequency hopping behavior vector, completing internal carrier switching, resetting modulation parameters, and initializing the forward error correction mechanism. The instruction sequence not only controls the frequency band switching but also embeds information about the previous and next synchronization mechanisms, ensuring that the clocks between nodes remain consistent and aligned with the protocol after the jump, thereby maintaining link stability during frequency band changes and avoiding information errors or communication deadlocks.

[0047] Step S6: Perform state synchronization reconstruction based on the adaptive jump instruction sequence, and perform dynamic deviation correction and delay repair on the communication frequency band jump driving logic to perform distribution automation communication signal optimization operations.

[0048] In this embodiment, after the adaptive jump instruction sequence is sent between each communication node, each node must immediately enter the state reconstruction phase to ensure that the communication parameters and network protocol stack configuration after the frequency band jump are highly consistent with the new environment. This process includes: resetting the modulation mode, synchronizing the master-slave clock, refreshing the link identity information (such as link ID, frequency band number), enabling the latest error correction strategy and other operations. In the experiment, a total of 12 distributed nodes were set up in a simulated tunnel deployment environment. When jumping from the 420kHz frequency band to the 300kHz frequency band, the system detected that 10 nodes completed state synchronization within 10ms after sending the jump instruction. The reconstruction of the other two nodes took slightly longer due to multipath reflection interference. The system uses a signal confirmation mechanism to ensure that all nodes restore communication synchronization status on the same frequency band. After completing the frequency band hopping and synchronous reconstruction, the system does not end the monitoring process. Instead, it continues to collect link stability indicators, including: the difference in communication response delay before and after the frequency band hopping, the trend change in bit error rate, and the packet loss rate correction curve. These parameters are directly related to whether the frequency hopping behavior truly improves communication quality or creates new interference problems.

[0049] In the experimental setup, in the 300kHz frequency band, the response delay of node D after hopping increased from the original 19ms to 24ms. Although it did not exceed the delay threshold, the system identified its abnormal status and recorded the weak synchronization behavior of the node in this frequency band. This fine-grained link monitoring can provide an important basis for the subsequent optimization of the frequency band hopping logic. By comparing the timing of link status data, the system can accurately determine the attenuation adaptability and link self-healing capabilities of a certain frequency band at a certain node, thereby further adjusting the frequency hopping strategy and optimizing the overall spectrum utilization efficiency of the system. Based on the analysis results of link status changes, the system activates the dynamic deviation correction module of the frequency band hopping drive logic. This module fine-tunes parameters such as the priority judgment weight, delay trigger threshold, and frequency hopping cooldown time in the current frequency hopping strategy, and automatically adapts to changes in environmental interference through a learnable control algorithm. Taking this experiment as an example, the 400kHz frequency band originally ranked low in the hopping priority, but after delay comparison analysis, it was found that its short-term stability was higher than that of the 300kHz frequency band. Therefore, the system raised its priority by one level and adjusted the next frequency hopping trigger threshold to within 15ms to improve response sensitivity. This correction mechanism uses the actual effect of the current frequency hopping behavior as a reference to avoid the frequency band optimization strategy falling into the problem of "theoretical optimality" but "practical incompatibility". At the same time, the system also synchronously corrects the generation strategy of subsequent hopping instruction sequences based on the correction results, realizing a true "frequency band hopping closed-loop control" to ensure the long-term stable operation of distribution communication.

[0050] In this embodiment, refer to Figure 2 , is a flowchart of the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include:

[0051] Identify all distribution communication nodes in the underground distribution system;

[0052] Calculating the underground spatial location coordinates of the power distribution communication nodes, performing a global distribution analysis of the node locations, and constructing an underground power distribution communication node distribution network;

[0053] Conduct multi-band data transmission tests on underground power distribution communication node distribution networks based on perturbation frequency test packages, and collect multi-band power distribution communication signals received by each node;

[0054] Performing environmental noise adaptive filtering on the multi-band power distribution communication signal to generate a filtered noise-reduced multi-band communication signal;

[0055] Dynamic spatiotemporal attenuation analysis is performed on filtered and denoised multi-band communication signals, and a multi-band electromagnetic spatiotemporal attenuation matrix is ​​constructed.

[0056] In this embodiment, in an underground power distribution system, distribution communication nodes are key components for data collection, control command issuance, and equipment status monitoring. They typically include ring main unit (FTU), distribution transformer monitoring units (TTU), communication repeaters, and underground base stations. Identifying these communication nodes requires first obtaining GIS data for the underground power distribution system infrastructure, cable channel layout diagrams, equipment wiring diagrams, and the layout coordinates of each intelligent terminal. This data is often sourced from the distribution company's SCADA system, distribution automation dispatching platform, or equipment installation records. The identification method integrates BIM modeling technology with laser radar (LiDAR) mapping data to accurately locate node locations. The BIM model captures the equipment's installation hierarchy and structural relationships, while the LiDAR scanning data is used to infer the actual physical locations of the nodes. The identification process includes: first, scanning underground channels and cable wells to extract 3D point cloud data; second, performing semantic segmentation on the point cloud data to identify key nodes such as intelligent terminals, cable junctions, and branch connections; third, matching the intelligent device information registered in the GIS equipment database to establish a one-to-one correspondence; and finally, generating a preliminary list of underground power distribution communication nodes. Furthermore, manual verification of the actual operating status of the equipment is required. Heartbeat packets are sent from the dispatch terminal to confirm whether the equipment is online, and offline or inactive nodes are eliminated to ensure the accuracy of the communication network identification results. A typical test area is a typical urban distribution network. For example, an underground cable tunnel in one location is approximately 1.5 km long and has 35 nodes, including 10 trunk nodes, 15 branch nodes, and 10 terminal nodes. After all communication nodes are identified, their three-dimensional coordinates are determined to establish a complete spatial topology model of the underground distribution communication network.

[0057] High-precision GNSS combined with IMUs (inertial navigation systems suitable for underground operations) are used to fit underground tunnel trajectories and map identified nodes to an underground coordinate system. Due to severe signal shielding in the underground environment, GNSS cannot be used directly underground. Therefore, a method using RTK-based ground positioning and relative displacement inertial compensation is typically employed: Position calibration is performed at the wellhead, and displacement information is collected point by point along the path using inertial measurement equipment. The three-dimensional coordinates of each communication node can be represented as (x, y, z), where the z coordinate represents depth or floor level. The system inputs all node coordinates into a graph-based spatial model to construct a connectivity graph between communication nodes. Connectivity analysis uses a shortest path tree algorithm (such as Dijkstra) and a minimum spanning tree (MST) model, with cable connections as graph edges and nodes as graph vertices, to form an underground communication topology network. Global node distribution analysis metrics include node density (number of nodes per 100-meter path), average path length, maximum hop count, connectivity index, and redundant link ratio. For example, the average node density in one area was measured at 4 nodes per 150 meters, the average network path hop count was 3, and the connectivity reached 92.5%. This analysis will serve as the spatial foundation for subsequent multi-band communication testing and the design of anti-fading strategies. After establishing the communication network model, the next step is to test the propagation characteristics of different frequency bands in the underground communication network. To this end, a perturbation frequency test packet method is employed: test signal packets are sent to each node in the network at different frequencies, and parameters such as reception quality, signal-to-noise ratio, and bit error rate are measured at multiple receiving nodes. The test frequency bands range from 10 kHz to 10 MHz, covering three communication segments: low frequency (10–150 kHz), medium frequency (150 kHz–1 MHz), and high frequency (1 MHz–10 MHz), corresponding to the narrowband, medium frequency, and wideband communication bands in power line carrier (PLC) communication, respectively. The test signal uses a timestamped modulated signal (such as BPSK or OFDM) and an embedded header for synchronization and identification at the receiving end. The test solution employs a master-slave broadcast structure: the master node sequentially sends test packets at a set frequency, and the remaining nodes record the received data according to the protocol.

[0058] The test cycle was set to 30 seconds per frequency band, repeated three times to reduce random errors. In actual testing, the node receiving module was connected to a signal strength detector (RSSI module) and a bit error analyzer. After each communication test, each node's reception data, including reception strength, packet loss rate, and latency, was recorded and sent to a central analysis system. In a typical experiment, the 10 kHz frequency band signal could cover the entire backbone channel but had high latency, while the 1 MHz signal had a low packet loss rate but could only cover within two hops. The test showed that concrete reflections, metal channel interference, and cable coupling interference in underground spaces significantly affected signal propagation. The test results provide basic data support for subsequent filtering and frequency hopping. After multi-band signal acquisition is completed, the next step is to perform environmental noise adaptive filtering to improve signal validity and stability. The main noise sources in underground power distribution environments include power frequency interference (50 Hz), high-order harmonics from switching power supplies, coupled signal crosstalk, metal echoes, and electromagnetic scattering. This filtering process uses multi-order wavelet filtering technology based on adaptive filters (LMS / ANC algorithms) and spectrum analysis. In the actual processing process, the original signal is first spectrally analyzed to identify the primary interference frequency band. A Fast Fourier Transform (FFT) is used to identify a significant noise peak between 20 and 60 kHz, which is then set as the target for dynamic filtering. The adaptive filtering process consists of three steps: 1) constructing an expected model of the target signal (e.g., an ideal communication packet pattern); 2) updating the filter coefficients using the least mean square error (LMS) algorithm; and 3) outputting an estimated signal with minimal error. Simultaneously, multi-resolution wavelet packet decomposition (WPD) technology is used to decompose the signal in both the time and frequency domains, removing transient interference while preserving the primary communication waveform. Parameters used include a sampling rate of 500 kHz, a filter order of 8, and an LMS step size of 0.005. The processed signal's average signal-to-noise ratio (SNR) improves by approximately 15–20 dB, and the bit error rate decreases by nearly 50%, providing a cleaner signal sample for subsequent attenuation analysis.

[0059] This model models the attenuation of filtered multi-band signals under different spatial and temporal conditions, constructing a multi-band electromagnetic spatiotemporal attenuation matrix. This matrix quantifies the propagation stability and reliability of different frequency bands in underground power distribution environments and serves as the core basis for frequency hopping scheduling and control. The construction method involves measuring metrics such as the received signal strength (RSSI), path loss (Path Loss), bit error rate (BER), and latency for each frequency band at all communication nodes, annotating the corresponding node coordinates (x, y, z) and timestamps. This multidimensional data is then input into a spatiotemporal analysis model. Common methods include multivariate regression analysis, neural network fitting (such as BP neural network), and interpolation algorithms (Kriging). The matrix dimensions are F × N × T, where F is the number of frequency bands, N is the number of nodes, and T is the number of test periods. Each element A(f,n,t) represents the signal attenuation value (in dB) for the fth frequency band at the nth node and time t. For example, in the 1 MHz frequency band, at node n=5, the attenuation value is 23 dB at 9:00 AM and 27 dB at 3:00 PM, reflecting that humid environments enhance the spatial attenuation of signals. The final visualization results, presented as heat maps or 3D surface plots, not only demonstrate the differences in spatial attenuation across frequency bands but also reveal the dynamic impact of temporal factors (such as diurnal temperature fluctuations and changes in channel electromagnetic load) on signal propagation. This matrix serves as input for designing an adaptive frequency hopping mechanism, dynamically selecting the optimal frequency for the current channel during communication and avoiding high-attenuation frequencies, thereby achieving stable, attenuation-resistant transmission in underground power distribution systems.

[0060] In this embodiment, the specific steps of performing dynamic spatiotemporal attenuation analysis on the filtered and noise-reduced multi-band communication signal and constructing a multi-band electromagnetic spatiotemporal attenuation matrix are as follows:

[0061] Identifying electromagnetic signal reflection parameters, path attenuation coefficients, and multipath interference characteristic data of the filtered and denoised multi-band communication signal, and generating electromagnetic scattering state characteristics for each node;

[0062] Performing three-dimensional point mapping of the underground power distribution communication node distribution network according to the electromagnetic scattering state characteristics, and constructing an electromagnetic scattering feature space model;

[0063] Perform multi-location signal attenuation trend analysis based on the electromagnetic scattering feature space model to generate signal attenuation trend information at different locations;

[0064] Calculating the frequency of the time-series communication signal in each frequency band of the electromagnetic scattering characteristic space model;

[0065] Performing frequency differential analysis on the frequency of the timing communication signal to extract a frequency fluctuation curve;

[0066] Perform periodic change analysis on the frequency fluctuation curve to generate frequency fluctuation periodic characteristics of multiple frequency bands;

[0067] Based on the signal attenuation trend information and the frequency fluctuation period characteristics, dynamic space-time dimension fusion is performed to construct a multi-band electromagnetic space-time attenuation matrix.

[0068] In this embodiment, after completing the filtering and noise reduction of the multi-band communication signal, it is necessary to further identify the electromagnetic scattering behavior characteristics of the signal in the underground space. This process mainly includes the extraction of three key parameters: reflection parameter (Reflection Coefficient), path attenuation coefficient (Path Loss Factor) and multipath interference characteristics (Multipath Interference Pattern). In the specific operation, by analyzing the time domain and frequency domain signal responses received by each node, comparing the theoretical model with the measured signal amplitude changes, and using the dual-path or multi-path propagation model (such as ITU-RP.1238) to infer the reflectivity parameters. In the experiment, a synchronization pulse or reference reference code (Reference Signal Block) is introduced into the test signal to infer the signal arrival time and phase difference of each path. The extraction of the path attenuation coefficient adopts the improved logarithmic distance attenuation model, and the definition formula is: Where n is the path loss exponent, and X is a Gaussian random variable (attenuation fluctuation). The optimal value of n is obtained through least squares fitting. Taking a 100-meter test path as an example, the path loss exponent n generally fluctuates between 2.3 and 4.7. Multipath interference feature extraction is based on the power delay profile of the received signal. By performing impulse response deconvolution on the received signal, the power distribution and delay information of multiple reflection paths are obtained, forming a multipath interference vector description for the node. Finally, for each node, the three parameters mentioned above are combined to construct the electromagnetic scattering state feature vector S_i = [R_i, PLF_i, MIF_i], forming a node-level electromagnetic propagation environment "fingerprint."

[0069] After obtaining the electromagnetic scattering eigenvectors for all nodes, they need to be mapped to the previously constructed 3D underground coordinate system to form a spatial characteristic model of electromagnetic propagation. This mapping process utilizes feature-enhanced 3D point cloud modeling technology. Specifically, the corresponding electromagnetic scattering state eigenvector S_i is appended to the 3D spatial coordinate point (x, y, z) of each communication node, forming an extended point description (x, y, z, R_i, PLF_i, MIF_i).

[0070] The mapping tool uses a point cloud analysis platform (such as PCL or Potree) to color map or contour model 3D data points based on scattering characteristics. To model electromagnetic interactions between nodes, a Gaussian kernel-based spatial weighting (SWR) is introduced. This means that the eigenvalue of each node depends not only on its own value but also on the influence of other nodes within a 5-10 meter radius. During the spatial model construction process, a voxel grid is also implemented, dividing the entire underground communication area into 1-cubic-meter volume cells. The average reflectivity and path loss in each cell are calculated, generating a 3D grid map with scattering feature annotations. This model provides a spatial basis for subsequent signal attenuation trends and frequency variations, visually demonstrating the varying scattering behavior of underground communication signals in different environmental structures, such as concrete shaft walls, metal pipes, or areas of concentrated humidity. The received strength values ​​of each node in different frequency bands are calculated, and a local path loss curve is fitted based on path length and propagation direction. The scatter plot is fitted using the locally weighted regression (LOESS) algorithm to generate a spatial trend surface for signal strength. Subsequently, the entire communication path is sliced ​​according to the propagation direction to generate attenuation gradient information for every 10 meters.

[0071] Furthermore, to reflect the temporal dynamics of the underground environment, the signal strength of certain frequency bands is sampled over time periods (day-night alternation, ambient humidity fluctuations), and a two-dimensional temporal-spatial attenuation trend graph is constructed. Temperature fluctuations in a certain area at night lead to increased condensation on metal pipelines, resulting in a 2-3 dB increase in signal attenuation. The analysis output is a database of signal attenuation trends across multiple frequency bands, locations, and time periods. This database provides the estimated signal loss rate for any frequency band at a specified location and time, supporting a dynamic communication strategy selection and scheduling mechanism. A timestamped communication frequency recording mechanism is used to record the frequency band selected by all communication nodes during each communication cycle, generating time series data. The currently used frequency (e.g., 950 kHz, 3.5 MHz, etc.) and its corresponding usage time period are counted for each node within each communication cycle (e.g., scheduling every 30 seconds). To extract the correlation between actual frequency usage and channel conditions, a frequency-environment adaptation regression analysis model is employed. This model analyzes the probability of each frequency being selected or hopped under different environmental conditions (e.g., specific scattering coefficient combinations). The final output is a time-series graph of the usage changes for each frequency band, annotating the occurrence points of each frequency hop and the environmental status indicators before and after the hop. This data provides time-series input for frequency fluctuation characteristics and hopping strategy optimization. After extracting the time-series communication frequency, frequency difference analysis is performed to obtain information such as the frequency hopping speed, fluctuation amplitude, and hopping pattern. This analysis uses the time difference method (Δf / Δt) and the calculation of the frequency fluctuation range within a sliding window. Specifically, a window width of 60 seconds and a sampling period of 5 seconds are set. The maximum and minimum frequency differences within each time window are calculated, and a continuous frequency fluctuation curve is plotted. To smooth the sudden interference caused by hopping, an exponentially weighted moving average (EWMA) filter algorithm is introduced to extract the main fluctuation trend. The fluctuation curve analysis results can be divided into three categories: stable segments (frequency remains essentially unchanged), fluctuating segments (frequent frequency hopping), and abnormal segments (violent frequency fluctuations accompanied by high bit error rates). Each segment has different implications for the communication strategy and needs to be labeled to prepare for periodic analysis. In actual communication testing, narrowband frequency bands (such as 150 kHz) have lower hopping frequencies, while mid- and high-frequency bands are more susceptible to interference and experience frequent frequency fluctuations, with amplitudes as large as 4 MHz. After obtaining the frequency fluctuation curve, its periodicity needs to be analyzed to identify the regular characteristics of the signal hopping. This analysis method combines Fourier transform (FFT) with autocorrelation analysis (ACF) to determine the dominant frequency variation period in different frequency bands. Applying a fast Fourier transform to the frequency fluctuation time series reveals the number of seconds and minutes that constitute a complete cycle. Some frequency bands exhibit a clear 24-hour periodicity, indicating significant influence from the workload of power equipment. Other frequency bands exhibit short-term fluctuations of 10-15 minutes, potentially due to intermittent operation of adjacent equipment or interference from electromagnetic switches.By calculating the peak position of the periodic ACF, the frequency fluctuation period of each frequency band can be accurately assessed. ACF analysis of the fluctuation sequence in the 3 MHz band revealed that the frequency hops occur every 180 seconds, corresponding to a system configuration period of 3 minutes. After completing all preliminary analyses, the signal attenuation trend information is finally integrated with the frequency periodic variation characteristics to construct a multi-band electromagnetic spatio-temporal attenuation matrix (Spatio-Temporal Electromagnetic Attenuation Matrix). The core function of this matrix is ​​to establish a high-dimensional reference model for frequency scheduling and channel selection.

[0072] The construction method is: with frequency (F), spatial position node (N) and time (T) as the three-dimensional coordinate axes, each unit A(F, N, T) in the matrix records the expected signal attenuation value (in dB) of the frequency at that node and at that time, and also adds the frequency fluctuation period and jump probability.

[0073] The data filling process adopts a multi-source information fusion strategy to fuse the following three data channels:

[0074] The signal attenuation trend graph provides the space-frequency attenuation value;

[0075] The temporal dynamics provided by the frequency hopping sequence;

[0076] The periodic feature provides predictive frequency availability.

[0077] To improve the model's real-time adaptability, a sliding window update mechanism was introduced. This refreshes the matrix every 10 minutes, re-assigning values ​​based on the latest channel status and environmental perception data. The resulting model serves as a core scheduling reference for frequency hopping. The system prioritizes communication on the frequency band with the lowest matrix value (i.e., the strongest signal and the lowest interference), achieving dynamic frequency avoidance and improved fading resistance.

[0078] In this embodiment, refer to Figure 3 , is a flowchart of the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include:

[0079] Extract the data packet sending timestamp of the transmission test;

[0080] Calculating a signal reception timestamp for each node based on the multi-band power distribution communication signal;

[0081] Calculate the response delay of each node based on the data packet sending timestamp and the signal receiving timestamp to obtain the full-band communication response delay of each node;

[0082] Performing multi-time window delay variation calculation on the full-band communication response delay to generate communication response delay values ​​of multiple time windows;

[0083] The communication response delays of multiple time windows are calculated by calculating the adjacent time window delay differences to obtain the adjacent time window delay difference characteristics;

[0084] Frequency delay sensitivity evaluation is performed based on the adjacent time window delay difference characteristics to generate a full-band delay sensitivity curve.

[0085] In this embodiment, during the multi-band performance testing of an underground power distribution communication network, the transmission time of each test packet must first be recorded to enable accurate comparison of subsequent reception response times. This process is accomplished by embedding a high-precision system timestamp in the packet header. Typically, a nanosecond-level timestamp recording mechanism (such as the IEEE 1588 Precision Time Protocol (PTP)) is used in conjunction with a GNSS synchronization module to ensure time consistency. The transmitting device, acting as the master control node, regularly broadcasts test packets to each communication node at fixed intervals (e.g., 500ms or 1s). The packet structure includes a sequence identifier, a transmitting frequency band identifier, current environmental information (e.g., temperature, humidity, node number), and the most critical transmission timestamp field. The transmission timestamp is directly sampled from the system master clock, with an accuracy of ±1µs. In actual experiments, separate transmissions are scheduled for each frequency band to avoid frequency overlap interference. Each broadcast cycle consists of 10 packets, with a 1s interval, lasting 5 minutes, covering five major communication frequency bands: 150kHz, 400kHz, 950kHz, 3.5MHz, and 6.7MHz. After completing the timestamp recording at the sender, the receiving node must record the reception time of each successfully received data packet. This step also relies on a high-precision timestamp system, requiring the receiver to have local clock synchronization capabilities with a synchronization error of no more than ±2µs relative to the sender's clock to prevent error accumulation. A clock synchronization module (such as a hybrid NTP+PTP architecture) is deployed in each receiving node, and a mechanism for instantaneous reception time sampling is incorporated into the packet reception logic. Upon receiving a test packet from each frequency band, the node immediately records the current local system time as the reception timestamp and binds it to the packet sequence number. During testing, each node receives an average of 100 to 300 valid data packets. Due to varying communication environment complexity, some frequency bands experience anomalies such as packet loss and sudden variations in reception delay. Therefore, the reception timestamp must be accompanied by reception quality indicators (such as SNR and bit error rate) as auxiliary analysis metrics. The reception timestamp sequence provides the endpoint time for subsequent response delay calculations. Its stability also reflects the stability of the communication link and serves as a key input parameter for time window delay variation statistics.

[0086] Calculate the actual communication response delay of each communication node in each frequency band: Where i represents the node number, j represents the packet number, and T_(recv_(i,j) ) and T_(send_j ) represent the receive and send timestamps, respectively. For each frequency band, the average and variance of the response delays for all packets on the same node are calculated to determine the average response delay and response stability indicator for that frequency band. For node A, the average response delay in the 950kHz band is 7.2ms with a variance of 1.4ms²; however, in the 3.5MHz band, the delay rises to 15.7ms, with significant fluctuations. The calculation also requires removing outliers, such as missing records caused by packet loss or extreme delays due to clock error anomalies. The IQR quartile rounding method is typically used to clean the raw delay data to ensure reliable statistical indicators. Finally, a response delay matrix D(i,f) is constructed for each node in each frequency band, where i represents the node number and f represents the communication frequency band. This matrix provides a delay baseline for subsequent multi-window dynamic analysis. After obtaining complete response delay data, in order to further characterize its time evolution characteristics, it is necessary to perform sliding window statistical analysis with time as the horizontal axis. Set the window length to 1 minute and the sliding step to 30 seconds to cover the entire test process. In each time window, extract the response delay corresponding to all successfully received data packets in the window, calculate the average delay, maximum value, minimum value and standard deviation of the window, and repeat this process to generate a set of time window sequences. Each window has its corresponding communication response delay statistic. In a 10-minute experiment, the aforementioned window settings yielded 19 time windows, each of which generated an average delay metric across five frequency bands, ultimately forming a three-dimensional delay tensor representing the time window, frequency band, and node. The changing trends of communication response delays between adjacent time windows are explored, particularly the sudden changes and slow changes. The calculated results are used to measure the temporal stability of communication delays for each node in each frequency band. Small delay differences (e.g., <0.5ms) indicate stable communication, while sudden increases (e.g., >3ms) typically indicate environmental changes or increased frequency interference.

[0087] To further analyze the trend of latency differences, a sliding average plus threshold discrimination method can be used to identify "stable segments," "variable segments," and "abnormal segments." If the difference values ​​of three consecutive sliding windows exceed 2 standard deviations from the mean, it is marked as an "abnormal fluctuation segment." The weighted average of the latency differences of all nodes in each frequency band during the entire test period is taken to form the latency sensitivity value S(f) for that frequency band. The formula is: Where N is the total number of nodes, and K is the total number of time windows. Higher sensitivity indicates a frequency band's more pronounced response to environmental disturbances or load fluctuations, resulting in poorer stability. Conversely, lower sensitivity indicates stronger interference immunity and suitability for critical communication control. During the analysis, delay sensitivity values ​​were normalized to a range of 0–1, and delay sensitivity curves for each frequency band were plotted as a core reference for dynamic frequency selection. While the low frequency band (150kHz) has a narrower bandwidth, it exhibits minimal delay fluctuation (a sensitivity coefficient of approximately 0.12), making it suitable for control command communications with high real-time requirements but small data volumes. Meanwhile, the mid- and high-frequency bands (3.5MHz and 6.7MHz) offer higher transmission rates but greater sensitivity fluctuations (a coefficient greater than 0.6) and should be used in conjunction with redundancy and frequency hopping mechanisms.

[0088] In this embodiment, reference Figure 4 The above is a flowchart of the detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include:

[0089] Analyze the received information of the multi-band power distribution communication signal based on the perturbation frequency test package, and extract the bit error rate and packet loss rate of each frequency band of each node;

[0090] Based on the bit error rate and packet loss rate of each frequency band, a safe and effective frequency band assessment is performed, and frequency band fitting is performed to construct a multi-dimensional frequency band hopping pool;

[0091] Based on the full-band delay sensitivity curve, the delay response heat distribution of the multi-dimensional band hopping pool is evolved to construct a full-band delay response heat distribution map;

[0092] Define the delay threshold, remove high-latency bands from the full-band delay heat response distribution map, and build a frequency band hopping optimization pool.

[0093] In this embodiment, to quantify the communication quality of the underground power distribution communication system in a real-world operating environment, it is necessary to conduct in-depth analysis of the reception performance of each communication node in different frequency bands based on "perturbation frequency test packets." Perturbation frequency test packets are data structures with a stable structure and consistent payload, widely used to detect link layer stability. They consist of a packet number, a frequency band identifier, a CRC checksum, a transmission timestamp, and a fixed control information field, with a length of 256 bytes. During the experiment, the test platform sent perturbation test packets in a cyclical manner across frequency bands (for example, with a 500ms interval between each frequency band, switching in a loop), with a fixed number of 100 packets sent per round. Based on the reception results, each receiving node recorded the number of successfully received packets in each frequency band in real time and detected the number of bit errors using a CRC checksum. During the statistical period, two metrics were recorded: the "packet loss rate," which is the proportion of packets that were not successfully received within a specified time period; and the "bit error rate," which is the percentage of bit errors among successfully received packets. Bit error statistics require bit-level accuracy. After accurately extracting the bit error rate and packet loss rate, the resulting data needs to be integrated and analyzed to identify the frequency bands that are "safe and available" for each node in the current underground environment. This assessment aims to construct a base frequency pool with flexible hopping capabilities, eliminating unstable bands with high bit error rates and high packet loss, thereby enhancing the overall system's interference resistance and communication continuity. Specifically, assessment thresholds are first defined: frequency bands with packet loss rates exceeding 20% ​​and bit error rates exceeding 1% are designated as "communication risk bands" and are temporarily excluded from the initial frequency hopping pool construction. The remaining frequency bands are then assigned a "safety level" rating, categorizing them into three levels: Level 1 (Excellent): Bit error rate <0.1%, packet loss rate <2%; Level 2 (Good): Bit error rate 0.1%-0.5%, packet loss rate <10%; and Level 3 (Available): Bit error rate 0.5%-1%, packet loss rate <20%.

[0094] To ensure continuity and switchability of the frequency hopping strategy, a clustering fitting method is used to integrate the frequency bands within each of the aforementioned levels. Common methods include the density-based DBSCAN clustering algorithm and K-means frequency band similarity fitting. Bands with similar performance are extracted as "band hopping units," thereby forming a "multi-dimensional band hopping pool" with dimensional expansion capabilities. While all bands in this constructed multi-dimensional band hopping pool meet acceptable bit error and packet loss metrics, significant differences exist in their communication latency response. This is particularly true in the complex physical structures of underground environments, where reflections, diffraction, and cable layout variations can cause significant latency fluctuations in certain bands. Therefore, the "latency response heat" evaluation dimension is introduced to comprehensively reflect the stability of each band in the time domain. The core of this step is to introduce a delay sensitivity curve based on the band hopping pool. This curve uses the "node-band-latency fluctuation" data obtained in the previous analysis to analyze the average latency level and latency variance of different bands within a specified time window, forming a heat map. The higher the heat value, the more sensitive the frequency band is to environmental changes and the more unstable the delay response; conversely, the more suitable it is for controlling communication paths. The system uses a two-dimensional heat map representation, with the horizontal axis representing the communication frequency band (in kHz or MHz), the vertical axis representing the node number or physical location information, and the color intensity representing the delay response heat level. The heat distribution map is dynamically refreshed through a sliding window mechanism that is updated every 30 seconds, giving it a time-varying capability, thereby capturing fluctuations in communication performance caused by underground environmental disturbances (such as power supply fluctuations, humidity changes, and structural vibrations). Based on the delay heat distribution map, the system needs to further focus on the "timeliness guarantee" of actual communication needs, that is, high-latency and unstable frequency bands must be eliminated from the frequency hopping strategy. This process is achieved by setting a delay response threshold and flexibly configuring the threshold according to different application scenarios. For example:

[0095] Real-time control services: The threshold is set to a maximum allowable delay of no more than 10ms and a delay variance of no more than 2ms.

[0096] Status upload services: The latency can be increased to 50ms, but the stability still requires a variance of less than 5ms.

[0097] Non-real-time scheduling services: The maximum delay tolerance range is 200ms, but the packet loss tolerance is lower.

[0098] The system configures corresponding threshold standards based on business needs, performs point-by-point comparison on the delay heat map, and removes frequency bands that do not meet the threshold requirements. The removal mechanism includes two methods: static filtering and dynamic shielding. Static filtering directly removes the frequency band from the frequency band hopping pool; dynamic shielding marks the frequency band as "temporarily unavailable" during a certain period of time, retaining it in the hopping pool but not participating in the current frequency switching. After the processing is completed, the system will form a "frequency band hopping preferred pool", which only contains a collection of frequency bands that meet the standards for communication quality (bit errors, packet loss), delay response, and time-varying heat performance. It is the core resource library of the entire adaptive frequency hopping communication strategy. The system will perform subsequent frequency hopping sequence generation, communication scheduling adjustment, and real-time link switching based on this preferred pool.

[0099] In this embodiment, the specific steps of defining a delay threshold, removing high-delay frequency bands from the full-band delay heat response distribution graph, and constructing a frequency band hopping optimization pool are as follows:

[0100] Define the delay threshold, identify high-delay frequency bands based on the full-band delay heat response distribution map, and extract high-delay frequency bands;

[0101] Eliminate high-latency frequency bands and build a frequency band hopping optimization pool;

[0102] Predicting the long-term attenuation trend of the high-delay frequency band to obtain a long-term attenuation trend curve of the high-delay frequency band;

[0103] Performing transient environmental disturbance analysis based on the long-term attenuation trend curve to identify transient environmental disturbance characteristics in a high-delay frequency band;

[0104] Perform disturbance feedback judgment based on the transient environmental disturbance characteristics and evaluate the delay attenuation self-recovery capability value in the high-delay frequency band;

[0105] Performing suboptimal frequency band screening based on the delay attenuation self-healing capability value to extract suboptimal frequency bands of high delay frequency bands;

[0106] Perform high-frequency delay retest management on suboptimal frequency bands, and update and optimize the frequency band hopping optimization pool in real time.

[0107] In this embodiment, when analyzing frequency band performance, a "delay response threshold" is set to identify unstable frequency bands exhibiting high latency within a specific time period. This threshold can be flexibly adjusted based on the application scenario, control requirements, and the characteristics of the underground communication environment. In actual testing, the delay threshold is typically set to define a "high-latency frequency band" as a single-point communication delay exceeding 15ms or a delay fluctuation (i.e., standard deviation) exceeding 5ms. For high-precision control services, the delay threshold requirement is more stringent, such as within 8ms. The system extracts all frequency bands that do not meet these thresholds from the constructed "Full-Band Delay Response Heat Map." The map is dynamically updated based on a sliding time window, making high-latency identification time-sensitive. To improve accuracy, the system incorporates a delay identification buffer mechanism. A frequency band is only officially recorded as a "high-latency frequency band" if it is marked as high latency in three consecutive sliding windows. This design effectively eliminates false positives caused by short-term, sudden disturbances. After identifying high-latency frequency bands, they must be removed from the "multi-dimensional frequency band hopping pool" used by the system's current frequency hopping control to ensure that the frequency hopping path does not fall into high-latency communication areas, thereby maintaining low latency and high link reliability. This removal process is performed in real time by the system's frequency band scheduling module, which updates the frequency band status at the data level. This removal method utilizes a "label-driven screening mechanism," assigning status labels to frequency bands in the hopping pool, such as "available," "risk," and "candidate for removal." High-latency frequency bands are assigned the "candidate for removal" label and placed in a temporary observation zone. Only when the latency of a frequency band remains above a threshold for multiple consecutive test cycles will it officially become "unavailable" and be completely removed from the frequency hopping scheduling path. To prevent frequency hopping resource depletion due to large-scale frequency band removal, the system introduces a frequency band percentage limit during the removal process. For the 30 available hopping bands, the total number of removed bands must not exceed 20%, meaning a maximum of six bands can be removed. After exceeding the limit, the overall communication system load and transmission delay tolerance must be assessed before deciding whether to further remove or implement frequency band replacement. Removing a frequency band does not mean completely abandoning it. To improve resource reuse in the communication system, the system needs to conduct long-term dynamic observations of these removed "high-latency bands" to identify whether their attenuation trends show signs of periodicity, recovery, or further deterioration. In this step, a time series modeling approach is used to predict the attenuation trend of high-latency bands. The data source is the change in latency response values ​​within multiple consecutive time windows (e.g., one window every 5 minutes, for a total of 6 hours of observation). Common forecasting models such as ARIMA or LSTM-based lightweight neural networks are used to fit and extrapolate the latency data series to derive latency trends over the next several hours. The forecast output is a "long-term attenuation trend curve for high-latency bands," which graphically illustrates the future trend of the frequency band's latency values, including peaks, setbacks, and fluctuation patterns.This result is used to determine whether the attenuation in the current frequency band is a recoverable issue caused by periodic environmental disturbances (such as periodic equipment startup and shutdown, or cyclical humidity fluctuations), or a persistent degradation caused by line aging or structural damage. To further distinguish whether a high-latency frequency band is caused by a persistent fault or a transient disturbance, the attenuation curve is subjected to "transient disturbance feature analysis." Transient disturbances typically manifest as short, sharp fluctuations in latency, with periodic rises and falls. They are often triggered by external temporary factors such as moisture accumulation near underground cables, electromagnetic interference, and vibration from mechanical construction. The system compares the frequency band's delay curve with the timestamp synchronization results of data from external environmental sensors (such as humidity, temperature, and electromagnetic radiation intensity) to identify whether the fluctuations are highly correlated with environmental disturbances. If the fluctuations exhibit typical characteristics such as short triggering, high-frequency rebound, and intermittent enhancement, the frequency band can be determined to be dominated by "transient disturbances." In one experiment, latency on a certain 470MHz frequency band spiked to 25ms when humidity rose above 85% at night and returned to normal levels after the humidity dropped. This characteristic was labeled a "humidity-sensitive transient disturbance." This analysis generates a "high-latency frequency band disturbance signature table," which includes disturbance type, triggering factor, and impact period, providing a basis for self-recovery capability assessment. Assessing a frequency band's "self-recovery" capability—that is, whether latency can automatically recover to usable levels after the disturbance is removed—is key to determining its return to the frequency hopping resource pool. This step utilizes a "disturbance feedback modeling mechanism" that combines the disturbance features extracted during the disturbance identification phase to quantify its self-recovery capability. Specifically, the system records the time required for the frequency band's latency to recover, the rate of delay reduction, and the final stable value from the onset of the disturbance, and correlates this with the disturbance intensity. A frequency band self-recovery scoring model is then established, outputting a "delay attenuation self-recovery capability value," typically ranging from 0 (no self-recovery) to 100 (full recovery). The delay value of a certain frequency band recovered from 30ms to 12ms within 10 minutes after the electromagnetic interference intensity decreased and remained stable. The self-healing ability score of this frequency band was 87, qualifying it to enter the "second-best candidate frequency band pool".

[0108] The system marks frequency bands with scores above a set threshold (e.g., 70) as "potentially resilient" for subsequent retesting and verification, as well as for planning suboptimal paths. Based on the self-healing capability assessment results, the system screens high-latency bands for those with partial resilience, moderate latency fluctuations, and stable attenuation. These are temporarily designated "suboptimal bands." While these bands lack primary communication capabilities, they can serve as backup paths when resources are limited or interference is abnormal. The screening criteria require a self-healing capability score greater than or equal to a threshold (e.g., 70) and a latency variance reduction of more than 30%. Furthermore, to ensure security, all suboptimal bands must meet basic communication requirements of a bit error rate of less than 1% and a packet loss rate of less than 15%. The output is a "suboptimal band list," with each band accompanied by a score, fluctuation frequency, and recommended application scenarios (e.g., for non-real-time uploads only). This list is set as the "alternative scheduling pool" in the communication scheduling module and automatically invoked when the primary band becomes unavailable or overloaded. Finally, to continuously assess whether suboptimal frequency bands have the potential to become preferred resources, the system must implement high-frequency delayed retest management. Unlike the five-minute detection cycle for ordinary frequency bands, the detection cycle for suboptimal frequency bands is shortened to one minute, with a higher frequency and faster response. The system will record the delay, variance, bit error rate, and other information from each round of retesting and use a sliding average method to calculate the latest score in real time. If a frequency band performs well in three consecutive test cycles and its self-healing ability score rises to above 85 points, the system will automatically migrate the band to the "preferred pool" and update the frequency hopping strategy table. This mechanism establishes a dynamic entry and exit mechanism, making frequency band resource management real-time, adaptive, and resilient, providing key support for the stable operation of the entire communication system in complex underground environments.

[0109] In this embodiment, step S4 includes the following steps:

[0110] Perform real-time environmental interference attenuation analysis on the frequency band hopping optimization pool based on the multi-band electromagnetic spatiotemporal attenuation matrix to extract the spatiotemporal attenuation characteristics of all frequency bands;

[0111] Performing attenuation state evolution on the spatiotemporal attenuation characteristics to obtain attenuation state evolution characteristics of each frequency band;

[0112] Performing dynamic frequency band hopping priority calculation based on the attenuation situation evolution characteristics, thereby obtaining the frequency band hopping priority of the frequency band hopping preferred pool;

[0113] A frequency band attenuation hopping threshold is defined, and a nonlinear frequency band hopping driving decision is made based on the frequency band hopping priority, thereby constructing a communication frequency band hopping driving logic.

[0114] In this embodiment, based on a constructed multi-band electromagnetic spatiotemporal attenuation matrix, the system extracts in real time the signal attenuation levels of all frequency bands within the preferred hopping pool at different nodes and time windows. This matrix, generated by comprehensively calculating the received signal strength, propagation path loss, and transmission time for each frequency band at each node, presents the complex interference pattern of the underground electromagnetic environment in both spatial and temporal dimensions. During the analysis process, the system updates the matrix in real time using a sliding time window mechanism (e.g., one window every 10 seconds) based on data collected by distributed receiving nodes, thereby reflecting the latest electromagnetic interference situation. The "spatiotemporal attenuation characteristics" of each frequency band include, but are not limited to, key indicators such as the spatial decay gradient of signal strength, the temporal decay rate, and spatial multipath distortion. If a frequency band experiences a signal strength drop of up to 18dB at three receiving points in the northern tunnel section within 30 seconds, and a rapid increase in packet loss rate exceeding 10%, it will be labeled as exhibiting "high spatial interference fluctuation." Based on these characteristics, the system automatically generates a "spatiotemporal attenuation label," providing accurate data support for subsequent frequency band sorting and hopping strategies. After obtaining spatiotemporal attenuation characteristics, the system further analyzes their temporal evolutionary trends and rhythms to determine whether a frequency band is experiencing sustained deterioration, periodic improvement, or stable fluctuations. The core of this step lies in "evolution modeling," which extracts time-driven trend characteristics from attenuation data. The system uses a sliding mean method, weighted time series comparison, and a trend turning point identification algorithm to comprehensively model attenuation data for each frequency band over multiple time periods. The system extracts information such as the slope of signal strength change, the difference between maximum and minimum values, and the frequency of disturbances over the past five minutes to determine whether the attenuation trend exhibits a sustained trend. In a real-world scenario, a 620 MHz frequency band exhibited slow linear attenuation over six consecutive time windows, with its signal strength decreasing from -78 dBm to -85 dBm. Simultaneously, the multipath interference index increased from 1.5 to 2.3. This change was identified as a "slowly varying and sustained attenuation evolutionary characteristic," and the frequency band was labeled "moderate concern." Ultimately, the system generates an "attenuation profile evolution label" for each frequency band, such as "rapid deterioration," "periodic stabilization," "short-term deterioration," and "delay improvement." This characteristic label provides a reference for evolving weights when setting frequency band hopping priorities. Setting frequency band hopping priorities is a key decision-making foundation for frequency hopping scheduling. In this step, the system uses each frequency band's "attenuation profile evolution label" as a basis, combining multiple performance indicators such as its real-time bit error rate, packet loss rate, and delay response heat, to perform a comprehensive scoring and ranking to form a hopping priority sequence. Priority calculation uses a weighted scoring mechanism, assigning weights to each characteristic. In the underground substation area, the signal delay is weighted 0.35, the attenuation rate 0.30, the bit error rate 0.20, and the stability score 0.15. The total score for each frequency band ranges from 0 to 100, with higher scores indicating higher priority, meaning it will be prioritized in the hopping path when a frequency band hop occurs.This mechanism ensures that the communication system balances multi-dimensional performance metrics in resource scheduling and can dynamically adjust to environmental changes. Frequency band priority results are updated in real time and noted in the frequency hopping schedule, driving subsequent hopping decisions. The final step is to build real-time triggerable frequency hopping control logic. In this step, the system first defines a "frequency band attenuation hopping threshold." This threshold triggers a hopping mechanism once the real-time attenuation characteristics or priority score of a frequency band exceed a preset threshold. This threshold setting is typically related to the type of communication service. For example, for load regulation tasks requiring millisecond-level response, the hopping threshold is lower (e.g., a hopping event occurs when the signal strength drops by more than 3dB or the latency fluctuation exceeds 4ms). Furthermore, to enhance the intelligence and adaptability of hopping paths, the system implements a "nonlinear hopping-driven decision model." This prioritizes hopping based not only on the current score but also on factors such as the rate of score change, hopping costs (such as band switching overhead), and historical hopping stability. This prevents system instability caused by frequent hopping and prevents performance degradation caused by long-term reliance on a single frequency band. The hopping logic is based on a priority curve, combined with mutation monitoring and trend prediction, to evaluate and decide on hopping within each time window (e.g., every 20 seconds). The system prioritizes the three optimal frequency bands from the "first-level preferred pool" to form a hopping candidate set. The system then compares their performance with the currently used frequency bands. If a significant performance improvement is found, the hopping is immediately implemented. The finalized "communication frequency band hopping drive logic" will be incorporated into the system's frequency band scheduling module, enabling highly responsive, environmentally adaptive, and highly anti-interference communication frequency hopping control, ensuring a stable and reliable link for the underground power distribution communication system.

[0115] In this embodiment, the specific steps of step S5 are:

[0116] Continuously collect real-time distribution communication monitoring data of underground distribution systems;

[0117] Performing a multi-index communication quality assessment on the real-time power distribution communication monitoring data to obtain a real-time communication quality assessment value;

[0118] The real-time communication quality evaluation value is judged instantly based on the preset communication quality standard value. When the real-time communication quality evaluation value is less than or equal to the preset communication quality standard value, the frequency hopping control is performed instantly according to the communication frequency band hopping driving logic, and the distribution communication node is given an independent frequency hopping behavior vector to generate an adaptive hopping instruction sequence.

[0119] In this embodiment, to achieve continuous and dynamic awareness of communication link status, the system first deploys high-frequency communication monitoring modules in each underground power distribution communication node. These modules collect real-time communication quality parameters within the current operating frequency band, including but not limited to received signal strength (RSSI), signal-to-noise ratio (SNR), bit error rate (BER), packet loss rate, round-trip delay (RTT), and multipath interference level (MPI). To ensure data timeliness and continuity, the collection cycle is set to every 500ms, and a rolling window buffer strategy is used to retain the last five minutes of data for trend calculation. This data is locally cached and regularly reported to a central communication evaluation unit. It can also be processed by edge computing nodes before being uploaded. For nodes buried in cable trenches or tunnels, temperature and humidity sensing modules and geomagnetic disturbance sensing modules are also deployed to analyze the impact of environmental factors on communication quality. By integrating and collecting this multi-source data, the system forms a complete real-time awareness mechanism for underground communication status. After monitoring data collection is completed, the system conducts a comprehensive assessment of the communication status of each node. The evaluation model utilizes a complex analysis process based on a weighted multi-metric evaluation mechanism. Its goal is to integrate multiple communication quality parameters into a quantifiable "communication quality assessment value" to facilitate subsequent frequency hopping decisions. Specifically, the system first normalizes each quality parameter, converting it into a communication stability index between 0 and 1. Then, based on the importance of the underground communication environment, these metrics are weighted and aggregated to form a single assessment result. For example, a weight of 0.35 is assigned to latency, 0.25 to bit error rate, 0.20 to RSSI fluctuation, 0.15 to packet loss rate, and 0.05 to multipath interference. During one monitoring cycle, a node experienced significant signal-to-noise ratio fluctuations, a bit error rate increase of 1.3%, and a latency increase of 12ms. The system calculated a comprehensive communication quality assessment value of 0.58, which falls below the established communication quality safety standard (e.g., 0.70), resulting in a "substandard" communication quality assessment. After obtaining the real-time communication quality assessment value, the system compares it with the preset communication quality standard. This standard value is typically set based on on-site communication design indicators, network QoS requirements, and typical interference thresholds. For example, the industry standard sets a minimum communication quality tolerance of 0.70. When the real-time evaluation value is lower than or equal to this standard value, it indicates that the communication link is suboptimal or even unreliable. At this point, the system immediately activates the "communication band hopping driving logic" and calls a candidate frequency band from the band hopping priority pool, preparing to perform a frequency hopping operation. This hopping logic references the previously established frequency band hopping priority ranking and, in combination with attenuation trends and latency heat maps, prioritizes the most stable and responsive frequency band. The system assigns independent frequency hopping tasks to each communication node through the edge controller, ensuring node-level autonomy in frequency hopping control. When multiple nodes are simultaneously affected by electromagnetic interference, the system allows each node to select its own optimal frequency band for frequency hopping, avoiding blind frequency band switching across the entire network.This frequency hopping process operates in milliseconds, with the total latency for mid-band switching and link recovery control measured to be less than 80ms. This effectively ensures robustness and service continuity for underground automated communications in sudden fading scenarios. To support the flexible execution of distributed frequency hopping strategies, the system assigns a "frequency hopping behavior vector" to each node requiring hopping. This vector defines the node's frequency hopping path, the order of alternate frequency bands, the expected stability level, and hopping constraints (such as the maximum number of handoffs and minimum dwell time) over several future time windows.

[0120] A node's frequency hopping behavior vector might be configured to prioritize hopping to 750MHz. If the signal-to-noise ratio (SNR) in this band does not improve within 10 seconds, the system automatically switches to 640MHz. The maximum number of consecutive hopping cycles is set to three, with a dwell time of at least six seconds. This strategy vector is bound to the frequency hopping decision module of the node's local scheduler to ensure predictable and autonomous policy execution. Once issued, the frequency hopping behavior vector is translated into an executable "adaptive hopping instruction sequence," including a set of commands such as frequency band switching instructions, transmit and receive parameter configuration, neighbor node synchronization requests, and status reports. This entire instruction sequence is encapsulated using a lightweight communication protocol, enabling command issuance and execution feedback within 3ms. This mechanism not only enables rapid triggering of communication frequency hopping but also enables strategic management and node-level customization of frequency hopping behavior. The resulting frequency hopping behavior system is not only adaptive, low-latency, and highly stable, but also provides underground communication systems with sustained robustness against attenuation in high-interference environments.

[0121] In this embodiment, the specific steps of step S6 are:

[0122] Embedding the jump state code and the synchronous reconstruction information based on the adaptive jump instruction sequence to construct the jump state self-encoding information;

[0123] Based on the power distribution communication node receiving the hopping state self-encoding information and identifying the frequency band state, the communication signal is synchronously reconstructed to complete the frequency band hopping process;

[0124] Performing real-time monitoring of the link synchronization status during the frequency band hopping process and extracting link synchronization status change information;

[0125] Optimizing the frequency hopping behavior strategy parameters based on the link synchronization state change information to generate frequency hopping behavior optimization parameters;

[0126] Based on the frequency hopping behavior optimization parameters, the communication frequency band hopping driving logic is dynamically corrected and delayed, and an intelligent signal hopping optimization strategy is constructed to perform distribution automation communication signal optimization operations.

[0127] In this embodiment, after the communication system sends the frequency hopping instruction sequence to each power distribution communication node, the system will automatically attach structured "hopping state self-encoding information" to the instruction sequence. The self-encoding information includes the frequency hopping status code, the original frequency band information, the new target frequency band identifier, the frequency hopping time window, the current position information of the node, the signal synchronization control flag, and the reconstruction control instruction. The generation of the hopping status code depends on the hopping logic tree and the historical frequency hopping record. For example, the status code can indicate whether it is a "first hopping", "retry hopping" or "strategic avoidance hopping" type, which is convenient for subsequent analysis and abnormality judgment. The synchronous reconstruction information is embedded with the signal reconstruction parameters corresponding to the frequency hopping target band, such as the pilot symbol structure, the initial frame number index, the multipath control instruction, etc.

[0128] To ensure the stability and anti-interference capability of coded information transmission, the self-encoded information utilizes redundant checking and a low-complexity error correction algorithm. It is broadcasted via a pre-defined auxiliary control channel between nodes. In the experiment, the packet size of this information was kept within 64 bytes, enabling delivery and confirmation within a 500ms response window. The embedded hopping state self-encoding ensures the structured, identifiable, and synchronously reconfigurable transmission of frequency hopping control information. Upon receiving the information carrying the hopping state self-encoding, the distribution communication node immediately initiates the frequency hopping preparation process. The first step is to read and parse the self-encoding structure, identify the hopping state code to determine the context for frequency hopping execution, and load the synchronous reconfiguration control parameters. Based on the preset physical parameters of the target frequency band (such as carrier spacing, frame format, and pilot structure), the node reconfigures its local communication module to operate in the target frequency band. The synchronous reconfiguration process includes key steps such as channel initialization, frequency offset adjustment, frame synchronization acquisition, and signal modulation mode switching. In some environments, due to poor channel characteristics, nodes also enable a preset "delay-tolerance mode," allowing for re-establishment through multiple sliding-window demodulations in the event of a frame header reception failure. To ensure clock and frequency synchronization between nodes, each node sends a synchronization request on the control channel of the target frequency band after a frequency hop and monitors the synchronization responses of neighboring nodes, forming a small-scale synchronization alignment mechanism. In actual deployment tests, the entire frequency hopping and synchronization re-establishment process takes an average of approximately 80ms, with the best case scenario of completing a frequency hopping link re-establishment within 45ms. To ensure the stability and continuity of the communication link after frequency hopping, the system monitors the link synchronization status in real time during each frequency hop. This monitoring process is implemented by a link health module integrated into each communication node, primarily monitoring key indicators such as successful communication restoration after the frequency hop, synchronization failures, signal acquisition time, synchronization loss rate, and resynchronization frequency. Synchronization status changes are recorded in real time through an event log and trended based on communication quality parameters (such as SNR stabilization time and first frame loss rate) for a period of 5 seconds after the frequency hop. In the data center platform or edge controller, this information is centrally collected, archived, and modeled to evaluate the performance of each frequency hopping strategy. For example, in an underground cable tunnel, actual testing showed that under conditions of humidity exceeding 85% and frequently fluctuating environmental electromagnetic interference, the frequency hopping link synchronization loss rate increased significantly, from an average of 2% to 8%. Capturing this synchronization state change information enables the system to determine whether there are structural issues during the frequency hopping process and provides a reliable basis for optimizing the strategy. After understanding the various synchronization state changes during the frequency hopping process, the system will perform targeted optimization of the frequency hopping strategy parameters, thereby generating new optimized parameters for frequency hopping behavior. This optimization process utilizes a hybrid modeling approach based on rule weighting and machine learning: the former identifies the triggering conditions for critical synchronization failures, while the latter performs sample learning and parameter prediction on frequency hopping performance under different environments.If a frequency band experiences poor synchronization stability after frequency hopping in a high-humidity environment, the system will lower its frequency hopping priority in that environment. Accordingly, frequency bands that have performed stably and recovered quickly after multiple frequency hopping cycles will have their weighting increased to a higher position in the preferred frequency band sequence.

[0129] Optimized parameters include, but are not limited to, frequency band dwell time, hopping trigger threshold, target frequency band selection granularity, synchronization confirmation timeout threshold, and control channel power callback factor. The resulting optimized parameters serve as core input for subsequent frequency hopping command generation and auto-encoding information construction, ensuring more stable and adaptable frequency hopping execution. Once optimized, the system dynamically corrects the existing hopping drive logic, correcting any inadequacies, delays, or handover errors in the original decision path. By adjusting the frequency hopping logic tree structure, replacing low-performing frequency bands, adding alternative heterogeneous link paths, and setting a delay margin that better adapts to new environments, the system achieves self-evolution of the hopping strategy. Furthermore, to address delay correction, the system introduces a dynamic delay compensation mechanism based on short-term state awareness. After frequency hopping resumes, it reduces initial communication delay by pre-activating the channel prediction cache, compressing preamble packets, and adjusting the retransmission strategy. The execution process of this intelligent signal hopping optimization strategy is embedded in the main communication control module and is triggered through both periodic execution and event-driven methods, ensuring both resilience to sudden changes and long-term adaptability. Experimental data shows that after the optimization strategy is deployed, the average frequency hopping communication recovery time is reduced by 17%, the link synchronization failure rate drops to below 3%, and the overall communication stability is significantly improved.

[0130] In this embodiment, a distribution automation communication signal optimization system for an underground environment is provided, which is used to execute the distribution automation communication signal optimization method for an underground environment as described above, including:

[0131] The space-time attenuation module is used to perform multi-band data transmission tests based on perturbation frequency test packages, collect multi-band power distribution communication signals, perform dynamic space-time attenuation analysis, and construct a multi-band electromagnetic space-time attenuation matrix.

[0132] A delay sensitivity module, configured to calculate the response delay of each node based on the multi-band power distribution communication signal, perform frequency delay sensitivity evaluation, and generate a full-band delay sensitivity curve;

[0133] A frequency band elimination module is used to perform safe and effective frequency band evaluation based on the multi-band power distribution communication signal, and eliminate high-delay frequency bands based on the full-band delay sensitivity curve to build a frequency band hopping optimization pool;

[0134] The frequency band hopping drive module is used to perform real-time environmental interference attenuation analysis on the frequency band hopping optimization pool based on the multi-band electromagnetic spatiotemporal attenuation matrix, and then make nonlinear frequency band hopping drive decisions to build the communication frequency band hopping drive logic;

[0135] The frequency hopping control module is used to perform real-time frequency hopping control according to the communication frequency band hopping drive logic, give the power distribution communication node an independent frequency hopping behavior vector, and generate an adaptive hopping instruction sequence;

[0136] The signal control optimization module is used to perform state synchronization reconstruction based on the adaptive jump instruction sequence, and dynamically correct the communication frequency band jump drive logic and repair the delay to perform distribution automation communication signal optimization operations.

[0137] The present invention is therefore intended to be illustrative and non-restrictive in all respects, with the scope of the invention being defined by the appended claims rather than the foregoing description, and all changes that come within the meaning and range of equivalents of the application documents are intended to be embraced therein.

[0138] The foregoing description is intended only to provide specific embodiments of the present invention, which are intended to enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is to be construed in the widest manner consistent with the principles and novel features disclosed herein.

Claims

1. A method for optimizing distribution automation communication signals for underground environments, characterized in that: The following steps are involved: Step S1: Perform a multi-band data transmission test based on a perturbation frequency test package to collect multi-band power distribution communication signals; And conduct dynamic space-time attenuation analysis to construct a multi-band electromagnetic space-time attenuation matrix; Step S2: performing node-by-node response delay calculation based on the multi-band power distribution communication signal, and performing frequency delay sensitivity evaluation to generate a full-band delay sensitivity curve; Step S3: performing a safe and effective frequency band assessment based on the multi-band power distribution communication signal, and eliminating high-delay frequency bands based on the full-band delay sensitivity curve to construct a frequency band hopping optimization pool; Step S4: performing real-time environmental interference attenuation analysis on the frequency band hopping optimization pool according to the multi-band electromagnetic spatiotemporal attenuation matrix, then making a nonlinear frequency band hopping driving decision, and constructing the communication frequency band hopping driving logic; Step S5: Performing instant frequency hopping control according to the communication frequency band hopping driving logic, assigning an independent frequency hopping behavior vector to the power distribution communication node, and generating an adaptive hopping instruction sequence; Step S6: Perform state synchronization reconstruction based on the adaptive jump instruction sequence, and perform dynamic deviation correction and delay repair on the communication frequency band jump driving logic to perform distribution automation communication signal optimization operations.

2. The method for optimizing distribution automation communication signals for underground environments according to claim 1, characterized in that: The specific steps of step S1 are: Identify all distribution communication nodes in the underground distribution system; Calculating the underground spatial location coordinates of the power distribution communication nodes, performing a global distribution analysis of the node locations, and constructing an underground power distribution communication node distribution network; Conduct multi-band data transmission tests on underground power distribution communication node distribution networks based on perturbation frequency test packages, and collect multi-band power distribution communication signals received by each node; Performing environmental noise adaptive filtering on the multi-band power distribution communication signal to generate a filtered noise-reduced multi-band communication signal; Dynamic spatiotemporal attenuation analysis is performed on filtered and denoised multi-band communication signals, and a multi-band electromagnetic spatiotemporal attenuation matrix is ​​constructed.

3. The method for optimizing distribution automation communication signals for underground environments according to claim 2, characterized in that: The specific steps of performing dynamic spatiotemporal attenuation analysis on the filtered and noise-reduced multi-band communication signal and constructing a multi-band electromagnetic spatiotemporal attenuation matrix are as follows: Identifying electromagnetic signal reflection parameters, path attenuation coefficients, and multipath interference characteristic data of the filtered and denoised multi-band communication signal, and generating electromagnetic scattering state characteristics for each node; Performing three-dimensional point mapping of the underground power distribution communication node distribution network according to the electromagnetic scattering state characteristics, and constructing an electromagnetic scattering feature space model; Perform multi-location signal attenuation trend analysis based on the electromagnetic scattering feature space model to generate signal attenuation trend information at different locations; Calculating the frequency of the time-series communication signal in each frequency band of the electromagnetic scattering characteristic space model; Performing frequency differential analysis on the frequency of the timing communication signal to extract a frequency fluctuation curve; Perform periodic change analysis on the frequency fluctuation curve to generate frequency fluctuation periodic characteristics of multiple frequency bands; Based on the signal attenuation trend information and the frequency fluctuation period characteristics, dynamic space-time dimension fusion is performed to construct a multi-band electromagnetic space-time attenuation matrix.

4. The method for optimizing distribution automation communication signals for underground environments according to claim 1, characterized in that: The specific steps of step S2 are: Extract the data packet sending timestamp of the transmission test; Calculating a signal reception timestamp for each node based on the multi-band power distribution communication signal; Calculate the response delay of each node based on the data packet sending timestamp and the signal receiving timestamp to obtain the full-band communication response delay of each node; Performing multi-time window delay variation calculation on the full-band communication response delay to generate communication response delay values ​​of multiple time windows; The communication response delays of multiple time windows are calculated by calculating the adjacent time window delay differences to obtain the adjacent time window delay difference characteristics; Frequency delay sensitivity evaluation is performed based on the adjacent time window delay difference characteristics to generate a full-band delay sensitivity curve.

5. The method for optimizing distribution automation communication signals for underground environments according to claim 1, characterized in that: The specific steps of step S3 are: Analyze the received information of the multi-band power distribution communication signal based on the perturbation frequency test package, and extract the bit error rate and packet loss rate of each frequency band of each node; Based on the bit error rate and packet loss rate of each frequency band, a safe and effective frequency band assessment is performed, and frequency band fitting is performed to construct a multi-dimensional frequency band hopping pool; Based on the full-band delay sensitivity curve, the delay response heat distribution of the multi-dimensional band hopping pool is evolved to construct a full-band delay response heat distribution map; Define the delay threshold, remove high-latency bands from the full-band delay heat response distribution map, and build a frequency band hopping optimization pool.

6. The method for optimizing distribution automation communication signals for underground environments according to claim 5, characterized in that: The specific steps of defining the delay threshold, removing high-delay frequency bands from the full-band delay heat response distribution map, and building the frequency band hopping optimization pool are as follows: Define the delay threshold, identify high-delay frequency bands based on the full-band delay heat response distribution map, and extract high-delay frequency bands; Eliminate high-latency frequency bands and build a frequency band hopping optimization pool; Predicting the long-term attenuation trend of the high-delay frequency band to obtain a long-term attenuation trend curve of the high-delay frequency band; Performing transient environmental disturbance analysis based on the long-term attenuation trend curve to identify transient environmental disturbance characteristics in a high-delay frequency band; Perform disturbance feedback judgment based on the transient environmental disturbance characteristics and evaluate the delay attenuation self-recovery capability value in the high-delay frequency band; Performing suboptimal frequency band screening based on the delay attenuation self-healing capability value to extract suboptimal frequency bands of high delay frequency bands; Perform high-frequency delay retest management on suboptimal frequency bands, and update and optimize the frequency band hopping optimization pool in real time.

7. The method for optimizing distribution automation communication signals for underground environments according to claim 1, characterized in that: The specific steps of step S4 are: Perform real-time environmental interference attenuation analysis on the frequency band hopping optimization pool based on the multi-band electromagnetic spatiotemporal attenuation matrix to extract the spatiotemporal attenuation characteristics of all frequency bands; Performing attenuation state evolution on the spatiotemporal attenuation characteristics to obtain attenuation state evolution characteristics of each frequency band; Performing dynamic frequency band hopping priority calculation based on the attenuation situation evolution characteristics, thereby obtaining the frequency band hopping priority of the frequency band hopping preferred pool; A frequency band attenuation hopping threshold is defined, and a nonlinear frequency band hopping driving decision is made based on the frequency band hopping priority, thereby constructing a communication frequency band hopping driving logic.

8. The method for optimizing distribution automation communication signals for underground environments according to claim 1, characterized in that: The specific steps of step S5 are: Continuously collect real-time distribution communication monitoring data of underground distribution systems; Performing a multi-index communication quality assessment on the real-time power distribution communication monitoring data to obtain a real-time communication quality assessment value; The real-time communication quality evaluation value is judged instantly based on the preset communication quality standard value. When the real-time communication quality evaluation value is less than or equal to the preset communication quality standard value, the frequency hopping control is performed instantly according to the communication frequency band hopping driving logic, and the distribution communication node is given an independent frequency hopping behavior vector to generate an adaptive hopping instruction sequence.

9. The method for optimizing distribution automation communication signals for underground environments according to claim 1, characterized in that: The specific steps of step S6 are: Embedding the jump state code and the synchronous reconstruction information based on the adaptive jump instruction sequence to construct the jump state self-encoding information; Based on the power distribution communication node receiving the hopping state self-encoding information and identifying the frequency band state, the communication signal is synchronously reconstructed to complete the frequency band hopping process; Performing real-time monitoring of the link synchronization status during the frequency band hopping process and extracting link synchronization status change information; Optimizing the frequency hopping behavior strategy parameters based on the link synchronization state change information to generate frequency hopping behavior optimization parameters; Based on the frequency hopping behavior optimization parameters, the communication frequency band hopping driving logic is dynamically corrected and delayed, and an intelligent signal hopping optimization strategy is constructed to perform distribution automation communication signal optimization operations.

10. A distribution automation communication signal optimization system for underground environments, characterized in that: The method for optimizing distribution automation communication signals for underground environments according to claim 1 comprises: The space-time attenuation module is used to perform multi-band data transmission tests based on perturbation frequency test packages, collect multi-band power distribution communication signals, perform dynamic space-time attenuation analysis, and construct a multi-band electromagnetic space-time attenuation matrix. A delay sensitivity module, configured to calculate the response delay of each node based on the multi-band power distribution communication signal, perform frequency delay sensitivity evaluation, and generate a full-band delay sensitivity curve; A frequency band elimination module is used to perform safe and effective frequency band evaluation based on the multi-band power distribution communication signal, and eliminate high-delay frequency bands based on the full-band delay sensitivity curve to build a frequency band hopping optimization pool; The frequency band hopping drive module is used to perform real-time environmental interference attenuation analysis on the frequency band hopping optimization pool based on the multi-band electromagnetic spatiotemporal attenuation matrix, and then make nonlinear frequency band hopping drive decisions to build the communication frequency band hopping drive logic; The frequency hopping control module is used to perform real-time frequency hopping control according to the communication frequency band hopping drive logic, give the power distribution communication node an independent frequency hopping behavior vector, and generate an adaptive hopping instruction sequence; The signal control optimization module is used to perform state synchronization reconstruction based on the adaptive jump instruction sequence, and dynamically correct the communication frequency band jump drive logic and repair the delay to perform distribution automation communication signal optimization operations.

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